Ever since the introduction of the computer in the 1940s, there has been intense interest devoted to artificially replicating one of the human mind's highest faculties-intelligence. This branch of computer science is known as artificial intelligence (AI), and its goal is to produce a machine that exhibits characteristics associated with human intelligence, such as language comprehension, problem solving, pattern recognition, learning, and reasoning from incomplete or uncertain information.
In the decades of research, the major approach into artificial intelligence has been and still remains the development of increasingly complex algorithms, faster computer hardware, and larger memories, databases, and knowledge bases. With this technology, artificial intelligence researchers have developed expert systems that perform well at specific tasks, such as playing chess or diagnosing medical conditions, as long as the procedures and objectives are precisely defined and do not change. Regardless of their sophistication, however, the capabilities of these systems reflect the intelligence of the designers, not the intelligence of the systems themselves.
The DEEP BLUE™ computer chess system developed by IBM™ is a case in point. The DEEP BLUE™ system employed a combination of computationally intensive brute-force searching of possible chess positions for several moves ahead, sophisticated scoring and searching heuristics, and a database of openings and end-games that was able to defeat the world's top-rated human chess player at the time. What chess audiences had witnessed, however, was merely a triumph of brute force computation to solve a particular problem, not a feat of general intelligence. As a test of machine intelligence, chess might seem like a reasonable choice, because the ability to play chess in humans is a strong indicator of the ability to apply one's intellect to other complex problems. However, a human capable of playing a good game of chess but incapable of solving basic problems in other areas would not be considered intelligent so much as autistic. The DEEP BLUE™ system has no other capabilities outside of chess, and its success in chess does not translate to success in achieving general intelligence.
Accordingly, programming computers to mimic a particular task is unlikely to achieve genuine artificial intelligence, no matter how skilled their programmers become at devising algorithms to solve clearly-defined problems. Rather, there is a need for a learning and problem solving capability that is dynamic, general-purpose, and adaptable to unexpected challenges in a changing environment.
One reason for this shortcoming of this approach is that it models only the outward behaviors of intelligence, not its underlying dynamics. Playing grandmaster level chess is a behavior of intelligent humans, but what makes them intelligent is how they think about chess. In some cases, simulating a few relevant behaviors of a system is sufficient for a practical, artificial alternative. Artificial turf, for instance, is a practical substitute for grass as a green playing surface for football, but if artificial turf is expected to photosynthesize, attract wildlife, or introduce nutrients into the soil, then green pieces of plastic polymers woven through webbed rubber lattices is a poor design. To create intelligence, it is desirable to model not just some of the behaviors of human brains, but how human brains do it.
Unfortunately, the human brain is too complex to model its dynamics directly by duplicating its structure and low-level functions. For example, the human brain is an intricate network of millions of interconnected neurons. The region responsible for intelligence, the cerebral cortex, itself has several dozen different areas, and each area has about six different layers. Each layer has its own composition of several different types of neurons, and each type of neuron responds to input stimuli in its own way with its own characteristic firing rate and intensity. The types, arrangements, and dynamics of neurons and the connections between neurons show enormous variation across the layers, areas, and regions of the brain. Although the neuroscientific literature is rich with descriptions of the different neurons and connection types, there are as yet no successful theories of how the mind emerges from the inner workings of the brain. Without understanding how the brain achieves intelligence, artificial intelligence researchers are having a very hard time reproducing intelligence by mimicking the brain.
Even simplified models of the human brain have not been successful for attaining general intelligence. For example, one approach is to simulate the action of the human brain by using artificial neural networks, consisting of connected “artificial neurons” arranged in layers. FIG. 18 shows an artificial neural network 1800, including an input layer 1801 of three artificial neurons 1803, 1805, and 1807, usually one hidden layer 1809 (artificial neurons 1811, 1813, 1815, and 1817), and an output layer 1819 with artificial neurons 1821 and 1823. Each layer 1801, 1809, and 1819 is fully interconnected and signal processing is feed-forward, meaning that each artificial neuron in a given layer receives input signals from every artificial neuron in the previous layer and transmits an output signal to every artificial neuron in the next layer. Each artificial neuron produces output as a function of the weighted sum of its inputs, and the output of the artificial neural network as a whole depends on all the connection weights. Although most artificial neural networks have more artificial neurons than depicted in FIG. 18, they are rarely more complex than this multi-layer, feed-forward structure.
Artificial neural networks operate in two successive stages: programming and execution. In the programming stage, the artificial neural network is trained by applying a set of training patterns, such as letters of an alphabet for optical character recognition, to the input layer 1801 and adjusting the connection weights of all the artificial neurons until the appropriate outputs are produced at the output layer 1819. When the programming stage is complete, the artificial neural network is ready for the execution stage, in which the artificial neural network is used to classify inputs applied to the artificial neural network in relation to the patterns previously presented in the training set. Artificial neural networks have had some success in recognizing and classifying patterns, especially in hand writing and other character recognition applications, and they are often very robust with respect to noisy data. However, once an artificial neural network enters the execution stage, its capability to recognize new patterns is fixed until its connection weights are completely reprogrammed on a new training set. For example, if the artificial neural network had been trained to optically recognize characters of an alphabet, augmenting the alphabet with a new character requires the connection weights in the artificial neural network to be reset and retrained from scratch.
Because of this inherent limitation, artificial neural networks, despite their superficial and terminological resemblance to biological neural systems, show little promise of progressing over other approaches to artificial intelligence. While artificial neural networks and expert systems are programmed differently, the result is fundamentally the same—a relatively static system that executes a precise and predetermined series of calculations but which cannot learn or exhibit other signs of intelligence.
As disclosed in U.S. Pat. No. 6,424,961, issued Jul. 23, 2002, entitled “Adaptive Neural Learning System,” the contents of which are incorporated by reference herein in their entirety, I have invented a neural learning system capable of readjusting its connection weights with negative and positive reinforcements based on the temporal proximity of neural firings and levels of system activity. Accordingly, my neural learning system is adaptive, able to adjust its programming during its execution to recognize new patterns or perform new tasks without having to reprogram the neural learning system from scratch for a new problem. Furthermore, instead of the basic feed-forward arrangement of conventional artificial neural networks, neurons in my neural learning system can be connected in feedback loops of various sizes and complexities.
Compared to conventional artificial neural networks, my neural learning system is much more capable of representing the complexity of the human brain, but it is still unclear how much of the complexity of the human brain needs to be simulated to achieve intelligence. Although the brain's complexity is certainly sufficient for achieving intelligence, it is possible that much of this complexity has nothing to do with intelligence, for example, being used for maintaining body temperature, coordinating muscle movements, and other activities. In fact, some of the brain's complexity may not be used at all, being the legacy of a lengthy evolutionary process that had to produce fully functional intermediaries at each generation from the less intelligent ancestors of humans.
Therefore, a lot of the brain's complexity may in fact be unnecessary except as an inevitable evolutionary by-product for achieving intelligence, indicating that a detailed replication of the exact structure of the human brain is not a practical avenue for success. Nevertheless, there still remains a long-felt need for a breakthrough in artificial intelligence, in which machines can be developed that are genuinely intelligent, not idiot savants capable of performing only one pre-programmed task extremely well.